In the field of artificially intelligent computer systems capable of answering questions posed in natural language, cognitive question answering (QA) systems (such as the IBM Watson™ artificially intelligent computer system or and other natural language question answering systems) process questions posed in natural language to determine answers and associated confidence scores based on knowledge acquired by the QA system. In operation, users submit one or more questions through a front-end application user interface (UI) or application programming interface (API) to the QA system where the questions are processed to generate answers that are returned to the user(s). When a large number of users are simultaneously submitting multiple questions (e.g., thousands of questions at any given time), traditional QA systems treat every question with the same level of priority in terms of prioritization so that the questions are processed in chronological order, but this can lead to inefficient allocation of the QA system resources, such as can occur when single “noisy neighbor” user asks multiple, narrowly focused questions directed to a subset of the ingested corpus, resulting in poor or uneven response processing by the QA system for other questions that might have similar or higher importance. While certain question prioritization schemes have been proposed which use one or more ad-hoc static priority field values associated with the incoming request to perform prioritization, such schemes are not applicable to the processing of natural language questions due to the non-deterministic nature of such questions. As a result, the existing solutions for efficiently prioritizing and processing questions are extremely difficult at a practical level.